Search

BACKGROUND: Diverse toxicants and mixtures that affect hormone responsive cells [endocrine disrupting chemicals (EDCs)] are highly pervasive in the environment and are directly linked to human disease. They often target the nuclear receptor family of transcription factors modulating their levels and activity. Many high-throughput assays have been developed to query such toxicants; however, single-cell analysis of EDC effects on endogenous receptors has been missing, in part due to the lack of quality control metrics to reproducibly measure cell-to-cell variability in responses. OBJECTIVE: We began by developing single-cell imaging and informatic workflows to query whether the single cell distribution of the estrogen receptor-a (ER), used as a model system, can be used to measure effects of EDCs in a sensitive and reproducible manner. METHODS: We used high-throughput microscopy, coupled with image analytics to measure changes in single cell ER nuclear levels on treatment with ~100 toxicants, over a large number of biological and technical replicates. RESULTS: We developed a two-tiered quality control pipeline for single cell analysis and tested it against a large set of biological replicates, and toxicants from the EPA and Agency for Toxic Substances and Disease Registry lists. We also identified a subset of potentially novel EDCs that were active only on the endogenous ER level and activity as measured by single molecule RNA fluorescence in situ hybridization (RNA FISH). DISCUSSION: We demonstrated that the distribution of ER levels per cell, and the changes upon chemical challenges were remarkably stable features; and importantly, these features could be used for quality control and identification of endocrine disruptor toxicants with high sensitivity. When coupled with orthogonal assays, ERsingle cell distribution is a valuable resource for high-throughput screening of environmental toxicants. [ABSTRACT FROM AUTHOR]

Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals. Author summary: Chemical contaminants or toxicants pose environmental and health-related risks for exposure. The ability to rapidly understand their biological impact, specifically on a key modulator of important physiological and pathological states in the human body is essential for diagnosing and avoiding undesirable health outcomes during environmental emergencies. In this study, we use advanced data analytics for creating statistical models that can accurately predict the endocrinological activity of toxic chemicals based on high throughput/high content image analysis data. We focus on a subclass of chemicals that affect the estrogen receptor (ER), which is a pivotal transcriptional regulator in health and disease. The multidimensional imaging data of these benchmark chemicals are used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, we evaluate linear and nonlinear classifiers for predicting the estrogenic activity of unknown compounds and use feature selection, data visualization, and model discrimination methodologies to identify the most informative features for the classification of ER agonists/antagonists. [ABSTRACT FROM AUTHOR]

In this short review we discuss the current view of how the estrogen receptor (ER), a pivotal member of the nuclear receptor superfamily of transcription factors, regulates gene transcription at the single cell and allele level, focusing on in vitro cell line models. We discuss central topics and new trends in molecular biology including phenotypic heterogeneity, single cell sequencing, nuclear phase separated condensates, single cell imaging, and image analysis methods, with particular focus on the methodologies and results that have been reported in the last few years using microscopy-based techniques. These observations augment the results from biochemical assays that lead to a much more complex and dynamic view of how ER, and arguably most transcription factors, act to regulate gene transcription. [ABSTRACT FROM AUTHOR]

Summary: Environmental exposures to chemically heterogeneous endocrine-disrupting chemicals (EDCs) mimic or interfere with hormone actions and negatively affect human health. Despite public interest and the prevalence of EDCs in the environment, methods to mechanistically classify these diverse chemicals in a high throughput (HT) manner have not been actively explored. Here, we describe the use of multiparametric, HT microscopy-based platforms to examine how a prototypical EDC, bisphenol A (BPA), and 18 poorly studied BPA analogs (BPXs), affect estrogen receptor (ER). We show that short exposure to BPA and most BPXs induces ERα and/or ERβ loading to DNA changing target gene transcription. Many BPXs exhibit higher affinity for ERβ and act as ERβ antagonists, while they act largely as agonists or mixed agonists and antagonists on ERα. Finally, despite binding to ERs, some BPXs exhibit lower levels of activity. Our comprehensive view of BPXs activities allows their classification and the evaluation of potential harmful effects. The strategy described here used on a large-scale basis likely offers a faster, more cost-effective way to identify safer BPA alternatives. [ABSTRACT FROM AUTHOR]

Using a chromatin immunoprecipitation-paired end diTag cloning and sequencing strategy, we mapped estrogen receptor α (ERα) binding sites in MCF-7 breast cancer cells. We identified 1,234 high confidence binding clusters of which 94% are projected to be bona fide ERα binding regions. Only 5% of the mapped estrogen receptor binding sites are located within 5 kb upstream of the transcriptional start sites of adjacent genes, regions containing the proximal promoters, whereas vast majority of the sites are mapped to intronic or distal locations (>5 kb from 5′ and 3′ ends of adjacent transcript), suggesting transcriptional regulatory mechanisms over significant physical distances. Of all the identified sites, 71% harbored putative full estrogen response elements (EREs), 25% bore ERE half sites, and only 4% had no recognizable ERE sequences. Genes in the vicinity of ERα binding sites were enriched for regulation by estradiol in MCF-7 cells, and their expression profiles in patient samples segregate ERα-positive from ERα-negative breast tumors. The expression dynamics of the genes adjacent to ERα binding sites suggest a direct induction of gene expression through binding to ERE-like sequences, whereas transcriptional repression by ERα appears to be through indirect mechanisms. Our analysis also indicates a number of candidate transcription factor binding sites adjacent to occupied EREs at frequencies much greater than by chance, including the previously reported FOXA1 sites, and demonstrate the potential involvement of one such putative adjacent factor, Sp1, in the global regulation of ERα target genes. Unexpectedly, we found that only 22%-24% of the bona fide human ERα binding sites were overlapping conserved regions in whole genome vertebrate alignments, which suggest limited conservation of functional binding sites. Taken together, this genome-scale analysis suggests complex but definable rules governing ERα binding and gene regulation. [ABSTRACT FROM AUTHOR]

The nuclear hormone receptor, estrogen receptor α (ERα), and mitogen-activated protein kinases (MAPKs) play key roles in hormone-dependent cancers, and yet their interplay and the integration of their signaling inputs remain poorly understood. In these studies, we document that estrogen-occupied ERα activates and interacts with extracellular signal-regulated kinase 2 (ERK2), a downstream effector in the MAPK pathway, resulting in ERK2 and ERα colocalization at chromatin binding sites across the genome of breast cancer cells. This genomic colocalization, predominantly at conserved distal enhancer sites, requires the activation of both ERα and ERK2 and enables ERK2 modulation of estrogen-dependent gene expression and proliferation programs. The ERK2 substrate CREB1 was also activated and recruited to ERK2-bound chromatin following estrogen treatment and found to cooperate with ERα/ERK2 in regulating gene transcription and cell cycle progression. Our study reveals a novel paradigm with convergence of ERK2 and ERα at the chromatin level that positions this kinase to support nuclear receptor activities in crucial and direct ways, a mode of collaboration likely to underlie MAPK regulation of gene expression by other nuclear receptors as well. [ABSTRACT FROM AUTHOR]

Abstract: A series of structurally simple bibenzyl-diol and stilbene-diol core molecules, structural analogs of the well-known hexestrol and diethylstilbestrol non-steroidal estrogens, were prepared and evaluated as estrogen receptor (ER) subtype-selective ligands. Analysis of their ERα and ERβ binding showed that certain substitution patterns engendered binding affinities that were >100-fold selective for ERβ. When further investigated in cell-based gene transcription assays, some molecules showed similarly high relative transcriptional potency selectivity in favor of ERβ. Interestingly, the most ERβ-selective molecules were those bearing non-polar substituents on one of the internal carbon atoms. These compounds should be useful probes for determining the physiological roles of ERβ, and they might lead to the development of more selective and thus safer pharmaceuticals. [Copyright &y& Elsevier]

Abstract: Methyl-piperidino-pyrazole (MPP), an estrogen receptor α (ERα)-selective antagonist we developed, has a basic side chain (BSC) attached to an ERα-selective agonist ligand, methyl-pyrazole-triol (MPT) through an ether linkage. To remove the possibility that metabolic cleavage of the BSC in MPP would regenerate MPT, we have replaced the N-piperidinylethoxy moiety with an N-piperidinylpropyl group, giving MPrP. This new analog retains the high ERα-selective binding affinity and antagonist potency of MPP. [Copyright &y& Elsevier]

• The impact of endocrine disrupting chemicals on the estrogen receptor is modeled using nonlinear classification techniques. • Single-cell level data are obtained via high throughput/high content imaging assay. • The imaging data are denoised and compressed via principal component analysis. • The proposed modeling framework achieves more than 94% testing accuracy with two classification algorithms. • The effects of data preparation on the model performance are investigated and quantified. Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset. [ABSTRACT FROM AUTHOR]

Abstract: To create estrogen receptor beta (ERβ)-selective ligands with improved biological characteristics, we have extended our investigations of structurally simple bibenzyl-core ligands by preparing a series of compounds in which one phenol is replaced by a 3-hydroxypyridine ring. These phenethyl pyridines were obtained by picoline anion alkylation, and compounds with different patterns of alkyl substitution on the central two carbon units were prepared. Binding affinities for ERα and ERβ were determined, and ligands with promising affinities and selectivities for ERβ were further tested for their gene transcriptional activity. Several compounds had high affinity selectivity and good potency selectivity in transcription assays. This study advances our understanding of compounds having ER-subtype selectivity and will help to direct efforts in developing novel ER ligands. [Copyright &y& Elsevier]